Exposure to toxins can lead to a wide range of adverse health effects, including respiratory problems, neurological disorders, cancer, and reproductive issues. Toxins can come from various sources, such as industrial ...Exposure to toxins can lead to a wide range of adverse health effects, including respiratory problems, neurological disorders, cancer, and reproductive issues. Toxins can come from various sources, such as industrial waste, agricultural runoff, and household chemicals. Therefore, detecting and monitoring toxins in the environment is crucial for protecting human health and the environment. This study aimed to evaluate the performance of Hememics biosensor system in detecting environmental toxins such as Ricin and Staphylococcal enterotoxin B (SEB) in mixed matrixes. When Ricin and SEB are spiked into soil, chopped lettuce, tap water, milk and serum, the biosensor was able to detect these toxins, without sample processing, at a level of detection comparable to lab testing with high sensitivity and specificity. Furthermore, Hememics biosensor system is designed to be network-enabled, which means that results can be transmitted to relevant agencies for quick decisions. This feature is crucial in cases where quick action is needed to prevent further contamination or exposure to harmful toxins.展开更多
The weld quality is significantly affected by the weld parameters(arc voltage, welding current, nozzle to plate distance and welding speed) in the submerged arc welding(SAW). Bead-on-plate welds were performed on stai...The weld quality is significantly affected by the weld parameters(arc voltage, welding current, nozzle to plate distance and welding speed) in the submerged arc welding(SAW). Bead-on-plate welds were performed on stainless steel plates by automated SAW machine. The experimental data were collected in accordance with the response surface methodology(RSM). In addition to RSM, the regression analysis was performed to set up inputeoutput relationships in the SAW process. It was found that weld parameters define the geometry of weld bead and determine the mechanical properties of the joint. The influence of the input variables on weld bead geometry is represented as graphs. It was found that an increment in voltage increases the bead width but decreases the bead height, whereas the current increment result-in an increment in bead height and no change in bead width. The bead width and height decrease with the increment in the welding speed. With an increment in the nozzle-to-plate distance, bead width decrease, but bead height increases. The value of bead hardness increases with the increment in current but the increment in voltage and travel speed does not have a significant influence on the bead hardness. The predictions from the mathematical model developed and the corresponding experimental results are having a fair agreement. Further, the genetic algorithm(GA) is also used for predicting the weld bead geometry.展开更多
We utilize the phenomenologically parameterized piecewise polytropic equations of state to study various neutron star properties.We investigate the compliance of these equations of state with several astronomical obse...We utilize the phenomenologically parameterized piecewise polytropic equations of state to study various neutron star properties.We investigate the compliance of these equations of state with several astronomical observations.We also demonstrate that the theoretical estimates of the fractional moment of inertia cannot explain all the pulsar glitches observed.We model the crust as a solid spheroidal shell to calculate the fractional moment of inertia of fast-spinning neutron stars.We also show that the braking index obtained in a simple magnetic dipole radiation model with a varying moment of inertia deviates significantly from the observed data.Future developments in both theory and observations may allow us to use the fractional moment of inertia and braking index as observational constraints for neutron star equation of state.展开更多
The Himalayan region is the treasure house of natural wealth,particularly of medicinal and aromatic plants.These plants are used by the Indian traditional healers for the past many centuries to treat various ailments ...The Himalayan region is the treasure house of natural wealth,particularly of medicinal and aromatic plants.These plants are used by the Indian traditional healers for the past many centuries to treat various ailments such as skin disorders,asthma,diabetes,snake bite,fever,pain,eye diseases,diarrhoea,indigestion,jaundice,burn,wound,liver disorder,CNS disorders and urinary tract infection.The indigenous traditional knowledge of medicinal plants and therapies of various local communities has been lost due to changes in traditional culture and the introduction of modern technologies.Therefore,it is essential to explore the traditional knowledge of the indigenous medicinal plants mainly in such areas where there is a severe threat to natural vegetation owing to human inhabitation.The present study aimed to explore the medicinal plants of Chakrata region(Jaunsar-Bawar Hills),Uttarakhand,India used in the folk medicine for the management of diabetes by Jaunsari Tribe.In a comprehensive feld survey,the information about the medicinal plants have been mainly collected from the traditional healers and other elderly people belong to the tribal community.All the information about the medicinal plants of the study area was documented in a feld book.Various tools have been used to collect the samples for identifcation purpose and the authentication of the plants was done with the help of taxonomists.The literature on these plants was also searched from online(PubMed and Scopus)as well as from some textbooks and Ayurvedic classical texts.The present survey-based work described a total of 54 plants belonging to 47 genera and 30 families used in the traditional medicine for the management of diabetes in Chakrata region.The information gathered from the local community revealed that the plants are efective in diabetes and one can use most of them without consulting a practitioner or traditional healer.The literature revealed that most of the surveyed plants are already used in the preparation of various antidiabetic formulations such as Chandraprabha vati,Nishamalaki chunra,Amritamehari churna and Nisakathakadi kashayam along with various patent drugs which are frequently prescribed by the Ayurvedic practitioners in India.The present study explored the traditional as well as scientifc knowledge on the antidiabetic plants used by the tribal community.The documented information on these plants can be further used by the scientifc community to develop new drugs/formulations with the help of modern techniques.展开更多
Data clustering is crucial when it comes to data processing and analytics.The new clustering method overcomes the challenge of evaluating and extracting data from big data.Numerical or categorical data can be grouped....Data clustering is crucial when it comes to data processing and analytics.The new clustering method overcomes the challenge of evaluating and extracting data from big data.Numerical or categorical data can be grouped.Existing clustering methods favor numerical data clustering and ignore categorical data clustering.Until recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods.However,these algorithms could not use the concept of categorical data for clustering.Following that,suggestions for expanding traditional categorical data processing methods were made.In addition to expansions,several new clustering methods and extensions have been proposed in recent years.ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them.This paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures.The parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also provided.Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable.展开更多
In this paper,the impact of additional food and two discrete delays on the dynamics of a prey-predator model is investigated.The interaction between prey and predator is considered as Holling Type-II functional respon...In this paper,the impact of additional food and two discrete delays on the dynamics of a prey-predator model is investigated.The interaction between prey and predator is considered as Holling Type-II functional response.The additional food is provided to the predator to reduce its dependency on the prey.One delay is the gestation delay in predator while the other delay is the delay in supplying the additional food to predators.The positivity,boundedness and persistence of the solutions of the system are studied to show the system as biologically well-behaved.The existence of steady states,their local and global asymptotic behavior for the non-delayed system are investigated.It is shown that(i)predator’s dependency factor on additional food induces a periodic solution in the system,and(ii)the two delays considered in the system are capable to change the status of the stability behavior of the system.The existence of periodic solutions via Hopf-bifurcation is shown with respect to both the delays.Our analysis shows that both delay parameters play an important role in governing the dynamics of the system.The direction and stability of Hopf-bifurcation are also investigated through the normal form theory and the center manifold theorem.Numerical experiments are also conducted to validate the theoretical results.展开更多
Train rolling stock and track inspections are necessary for the safe operation of the train.For this reason,a regular inspection of defects is required for the train rolling stock.The conventional defect detection met...Train rolling stock and track inspections are necessary for the safe operation of the train.For this reason,a regular inspection of defects is required for the train rolling stock.The conventional defect detection methods yield low efficiency,consume more time,are unre-liable,and are less cost-effective.These obstacles may be mitigated by integrating a machine vision-based inspection system(MVIS).This systematic literature review explores the landscape of railway defect detection methodologies,primarily focusing on leveraging image processing techniques.This comprehensive analysis encompasses many studies examining the evolution of image processing applications in the context of railway rolling stock and rail track defect detection.From traditional methods to the latest advancements,a nuanced understanding of the challenges and innovations in this domain is required.Key themes include utilizing computer vision algorithms,machine learning models,and deep learning techniques for enhanced accuracy in identifying defects.We delve into the intri-cacies of image acquisition,preprocessing,and feature extraction,shedding light on the pivotal role of these processes in refining defect detection systems.Also,the current gaps and opportunities for future research,emphasizing the need for standardized datasets,benchmarking methodologies,and the integration of emerging technologies,are high-lighted.This review not only consolidates the existing knowledge,but also serves as a road-map for researchers invested in advancing the field of railway defect detection.By synthesizing insights from many studies,this review contributes to a deeper understand-ing of the state-of-the-art in railway defect detection using image processing,fostering dia-logue and collaboration for improving railway safety and reliability.展开更多
The study of drainage behavior is essential for using waste material in geotechnical applications.In this study,sandy soil was replaced with waste foundry sand(WFS)at an incremental interval of 20%by weight.Permeabili...The study of drainage behavior is essential for using waste material in geotechnical applications.In this study,sandy soil was replaced with waste foundry sand(WFS)at an incremental interval of 20%by weight.Permeability(k)for each mix was acquired at three relative densities(RD),i.e.,65%,75%and 85%,by using the constant head method.Then the results were further processed with machine learning(ML)models to validate the experimental data.The experimental study demonstrated that k would decrease with the increase in relative density and WFS content.A rise in RD from 65%to 85%resulted in a substantial reduction of up to 140%in the value of k.Moreover,the complete replacement of sand with WFS reduced the value of k by 36%,51%and 57%for RD of 65%,75%and 85%,respectively.The total dataset of 90 observations was divided at a ratio of 63/13/15 into training/validation/testing datasets for ML-AI modeling.Input variables include percentage of sand(BS),replacement with WFS,total head(H),time interval(t)and outflow(Q);and k is the output variable.The methods of artificial neural network(ANN),random forest(RF),decision tree(DT)and multi-linear regression(MLR)are used for k prediction.It is found that the random forest approach performed outstandingly in these methods,with an R_(2) value of 0.9955.The performance of all the proposed methods was compared and verified with Taylor’s diagram.Sensitivity analysis showed that Q and R_(D) were the most influential parameters for predicting k values.展开更多
Objective:To evaluate the cardioprotective effects of sinomenine using the ischemia/reperfusion(I/R)rat model.Methods:Wistar rats were randomly divided into 6 groups:group Ⅰ with reperfusion,group Ⅱ perfused with si...Objective:To evaluate the cardioprotective effects of sinomenine using the ischemia/reperfusion(I/R)rat model.Methods:Wistar rats were randomly divided into 6 groups:group Ⅰ with reperfusion,group Ⅱ perfused with sinomenine,group Ⅲ perfused with 5-hydroxydecanoate(5-HD),group Ⅳ perfused with 5-HD+sinomenine,group Ⅴ perfused with L-nitro arginine methyl ester(L-NAME),group Ⅵ perfused with L-NAME+sinomenine.Myocardial ischemia was induced by interrupting the aortic blood supply for 30 min,followed by reperfusion(55 min).Cardiac,hepatic,antioxidant,and inflammatory parameters were assessed.Additionally,endothelin,tissue factor,platelet-activating factor,plasminogen activator inhibitor,plasma fibrinogen,and thromboxane B2 were also analyzed.Results:Administration of 5-HD or L-NAME,used as the selective antagonist of mitoKATP and NO system,respectively,resulted in significantly increased levels of premature ventricular complexes,lactate dehydrogenase,ventricular fibrillation,ventricular tachycardia,and arrhythmia intensity(P<0.05).In contrast,sinomenine significantly reduced the level of troponin Ⅰ,lactate dehydrogenase,creatine kinase,and creatine kinase MB compared to the 5-HD group and the L-NAME group(P<0.05).Additionally,sinomenine significantly reduced malondialdehyde level and enhanced the levels of superoxide dismutase,glutathione peroxidase,catalase,and glutathione/glutathione disulfide ratio(P<0.05).It also significantly suppressed the levels of endothelin-1,platelet-activating factor,tissue factor,plasminogen activator inhibitor 1,thromboxane B2,and plasma fibrinogen(P<0.05).Conclusions:These results suggest that sinomenine exhibits significant cardioprotection effects against I/R-induced cardiac injury in rats.展开更多
The correct diagnosis of heart disease can save lives,while the incorrect diagnosis can be lethal.The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches...The correct diagnosis of heart disease can save lives,while the incorrect diagnosis can be lethal.The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches,including deep learning.We used a dataset with 13 primary characteristics to carry out the research.Support vector machine and logistic regression algorithms are used to process the datasets,and the latter displays the highest accuracy in predicting coronary disease.Python programming is used to process the datasets.Multiple research initiatives have used machine learning to speed up the healthcare sector.We also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection risks.Using the accuracy and confusion matrix has resulted in some favorable outcomes.To get the best results,the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine(SVM)classification.展开更多
In Computer-Aided Detection(CAD)brain disease classification is a vital issue.Alzheimer’s Disease(AD)and brain tumors are the primary reasons of death.The studies of these diseases are carried out by Magnetic Resonan...In Computer-Aided Detection(CAD)brain disease classification is a vital issue.Alzheimer’s Disease(AD)and brain tumors are the primary reasons of death.The studies of these diseases are carried out by Magnetic Resonance Imaging(MRI),Positron Emission Tomography(PET),and Computed Tomography(CT)scans which require expertise to understand the modality.The disease is the most prevalent in the elderly and can be fatal in its later stages.The result can be determined by calculating the mini-mental state exam score,following which the MRI scan of the brain is successful.Apart from that,various classification algorithms,such as machine learning and deep learning,are useful for diagnosing MRI scans.However,they do have some limitations in terms of accuracy.This paper proposes some insightful pre-processing methods that significantly improve the classification performance of these MRI images.Additionally,it reduced the time it took to train the model of various pre-existing learning algorithms.A dataset was obtained from Alzheimer’s Disease Neurological Initiative(ADNI)and converted from a 4D format to a 2D format.Selective clipping,grayscale image conversion,and histogram equalization techniques were used to pre-process the images.After pre-processing,we proposed three learning algorithms for AD classification,that is random forest,XGBoost,and Convolution Neural Networks(CNN).Results are computed on dataset and show that it outperformed with exiting work in terms of accuracy is 97.57%and sensitivity is 97.60%.展开更多
The objective of this paper is to provide a better rendition of Generation Z purchase intentions of retail products through Facebook.The study gyrated around the favorable attitude formation of Generation Z translatin...The objective of this paper is to provide a better rendition of Generation Z purchase intentions of retail products through Facebook.The study gyrated around the favorable attitude formation of Generation Z translating into intentions to purchase retail products through Facebook.The role of antecedents of attitude,namely enjoyment,credibility,and peer communication was also explored.The main purpose was to analyze the F-commerce pervasiveness(retail purchases through Facebook)among Generation Z in India and how could it be materialized effectively.A conceptual fac¸ade was proposed after trotting out germane and urbane literature.The study focused exclusively on Generation Z population.The data were statistically analyzed using partial least squares structural equation modelling.The study found the proposed conceptual model had a high prediction power of Generation Z intentions to purchase retail products through Facebook verifying the materialization of F-commerce.Enjoyment,credibility,and peer communication were proved to be good predictors of attitude(R^(2)=0.589)and furthermore attitude was found to be a stellar antecedent to purchase intentions(R^(2)=0.540).展开更多
We live in an age where everything around us is being created.Data generation rates are so scary,creating pressure to implement costly and straightforward data storage and recovery processes.MapReduce model functional...We live in an age where everything around us is being created.Data generation rates are so scary,creating pressure to implement costly and straightforward data storage and recovery processes.MapReduce model functionality is used for creating a cluster parallel,distributed algorithm,and large datasets.The MapReduce strategy from Hadoop helps develop a community of non-commercial use to offer a new algorithm for resolving such problems for commercial applications as expected from this working algorithm with insights as a result of disproportionate or discriminatory Hadoop cluster results.Expected results are obtained in the work and the exam conducted under this job;many of them are scheduled to set schedules,match matrices’data positions,clustering before determining to click,and accurate mapping and internal reliability to be closed together to avoid running and execution times.Mapper output and proponents have been implemented,and the map has been used to reduce the function.The execution input key/value pair and output key/value pair have been set.This paper focuses on evaluating this technique for the efficient retrieval of large volumes of data.The technique allows for capabilities to inform a massive database of information,from storage and indexing techniques to the distribution of queries,scalability,and performance in heterogeneous environments.The results show that the proposed work reduces the data processing time by 30%.展开更多
文摘Exposure to toxins can lead to a wide range of adverse health effects, including respiratory problems, neurological disorders, cancer, and reproductive issues. Toxins can come from various sources, such as industrial waste, agricultural runoff, and household chemicals. Therefore, detecting and monitoring toxins in the environment is crucial for protecting human health and the environment. This study aimed to evaluate the performance of Hememics biosensor system in detecting environmental toxins such as Ricin and Staphylococcal enterotoxin B (SEB) in mixed matrixes. When Ricin and SEB are spiked into soil, chopped lettuce, tap water, milk and serum, the biosensor was able to detect these toxins, without sample processing, at a level of detection comparable to lab testing with high sensitivity and specificity. Furthermore, Hememics biosensor system is designed to be network-enabled, which means that results can be transmitted to relevant agencies for quick decisions. This feature is crucial in cases where quick action is needed to prevent further contamination or exposure to harmful toxins.
基金supported by PEC University, Punjab and Invertis University, Bareilly, (U.P.), India
文摘The weld quality is significantly affected by the weld parameters(arc voltage, welding current, nozzle to plate distance and welding speed) in the submerged arc welding(SAW). Bead-on-plate welds were performed on stainless steel plates by automated SAW machine. The experimental data were collected in accordance with the response surface methodology(RSM). In addition to RSM, the regression analysis was performed to set up inputeoutput relationships in the SAW process. It was found that weld parameters define the geometry of weld bead and determine the mechanical properties of the joint. The influence of the input variables on weld bead geometry is represented as graphs. It was found that an increment in voltage increases the bead width but decreases the bead height, whereas the current increment result-in an increment in bead height and no change in bead width. The bead width and height decrease with the increment in the welding speed. With an increment in the nozzle-to-plate distance, bead width decrease, but bead height increases. The value of bead hardness increases with the increment in current but the increment in voltage and travel speed does not have a significant influence on the bead hardness. The predictions from the mathematical model developed and the corresponding experimental results are having a fair agreement. Further, the genetic algorithm(GA) is also used for predicting the weld bead geometry.
基金partly supported by the SPARK program of IIT Roorkee(India)。
文摘We utilize the phenomenologically parameterized piecewise polytropic equations of state to study various neutron star properties.We investigate the compliance of these equations of state with several astronomical observations.We also demonstrate that the theoretical estimates of the fractional moment of inertia cannot explain all the pulsar glitches observed.We model the crust as a solid spheroidal shell to calculate the fractional moment of inertia of fast-spinning neutron stars.We also show that the braking index obtained in a simple magnetic dipole radiation model with a varying moment of inertia deviates significantly from the observed data.Future developments in both theory and observations may allow us to use the fractional moment of inertia and braking index as observational constraints for neutron star equation of state.
基金supported by National Medicinal Plants Board,Ministry of AYUSH,Govt.of India(Grant No.Z.18017/187/CSS/R&D/UK-01/2017-18-NMPB-IV A).
文摘The Himalayan region is the treasure house of natural wealth,particularly of medicinal and aromatic plants.These plants are used by the Indian traditional healers for the past many centuries to treat various ailments such as skin disorders,asthma,diabetes,snake bite,fever,pain,eye diseases,diarrhoea,indigestion,jaundice,burn,wound,liver disorder,CNS disorders and urinary tract infection.The indigenous traditional knowledge of medicinal plants and therapies of various local communities has been lost due to changes in traditional culture and the introduction of modern technologies.Therefore,it is essential to explore the traditional knowledge of the indigenous medicinal plants mainly in such areas where there is a severe threat to natural vegetation owing to human inhabitation.The present study aimed to explore the medicinal plants of Chakrata region(Jaunsar-Bawar Hills),Uttarakhand,India used in the folk medicine for the management of diabetes by Jaunsari Tribe.In a comprehensive feld survey,the information about the medicinal plants have been mainly collected from the traditional healers and other elderly people belong to the tribal community.All the information about the medicinal plants of the study area was documented in a feld book.Various tools have been used to collect the samples for identifcation purpose and the authentication of the plants was done with the help of taxonomists.The literature on these plants was also searched from online(PubMed and Scopus)as well as from some textbooks and Ayurvedic classical texts.The present survey-based work described a total of 54 plants belonging to 47 genera and 30 families used in the traditional medicine for the management of diabetes in Chakrata region.The information gathered from the local community revealed that the plants are efective in diabetes and one can use most of them without consulting a practitioner or traditional healer.The literature revealed that most of the surveyed plants are already used in the preparation of various antidiabetic formulations such as Chandraprabha vati,Nishamalaki chunra,Amritamehari churna and Nisakathakadi kashayam along with various patent drugs which are frequently prescribed by the Ayurvedic practitioners in India.The present study explored the traditional as well as scientifc knowledge on the antidiabetic plants used by the tribal community.The documented information on these plants can be further used by the scientifc community to develop new drugs/formulations with the help of modern techniques.
基金supporting project number(RSP2022R498),King Saud University,Riyadh,Saudi Arabia.
文摘Data clustering is crucial when it comes to data processing and analytics.The new clustering method overcomes the challenge of evaluating and extracting data from big data.Numerical or categorical data can be grouped.Existing clustering methods favor numerical data clustering and ignore categorical data clustering.Until recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods.However,these algorithms could not use the concept of categorical data for clustering.Following that,suggestions for expanding traditional categorical data processing methods were made.In addition to expansions,several new clustering methods and extensions have been proposed in recent years.ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them.This paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures.The parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also provided.Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable.
文摘In this paper,the impact of additional food and two discrete delays on the dynamics of a prey-predator model is investigated.The interaction between prey and predator is considered as Holling Type-II functional response.The additional food is provided to the predator to reduce its dependency on the prey.One delay is the gestation delay in predator while the other delay is the delay in supplying the additional food to predators.The positivity,boundedness and persistence of the solutions of the system are studied to show the system as biologically well-behaved.The existence of steady states,their local and global asymptotic behavior for the non-delayed system are investigated.It is shown that(i)predator’s dependency factor on additional food induces a periodic solution in the system,and(ii)the two delays considered in the system are capable to change the status of the stability behavior of the system.The existence of periodic solutions via Hopf-bifurcation is shown with respect to both the delays.Our analysis shows that both delay parameters play an important role in governing the dynamics of the system.The direction and stability of Hopf-bifurcation are also investigated through the normal form theory and the center manifold theorem.Numerical experiments are also conducted to validate the theoretical results.
文摘Train rolling stock and track inspections are necessary for the safe operation of the train.For this reason,a regular inspection of defects is required for the train rolling stock.The conventional defect detection methods yield low efficiency,consume more time,are unre-liable,and are less cost-effective.These obstacles may be mitigated by integrating a machine vision-based inspection system(MVIS).This systematic literature review explores the landscape of railway defect detection methodologies,primarily focusing on leveraging image processing techniques.This comprehensive analysis encompasses many studies examining the evolution of image processing applications in the context of railway rolling stock and rail track defect detection.From traditional methods to the latest advancements,a nuanced understanding of the challenges and innovations in this domain is required.Key themes include utilizing computer vision algorithms,machine learning models,and deep learning techniques for enhanced accuracy in identifying defects.We delve into the intri-cacies of image acquisition,preprocessing,and feature extraction,shedding light on the pivotal role of these processes in refining defect detection systems.Also,the current gaps and opportunities for future research,emphasizing the need for standardized datasets,benchmarking methodologies,and the integration of emerging technologies,are high-lighted.This review not only consolidates the existing knowledge,but also serves as a road-map for researchers invested in advancing the field of railway defect detection.By synthesizing insights from many studies,this review contributes to a deeper understand-ing of the state-of-the-art in railway defect detection using image processing,fostering dia-logue and collaboration for improving railway safety and reliability.
文摘The study of drainage behavior is essential for using waste material in geotechnical applications.In this study,sandy soil was replaced with waste foundry sand(WFS)at an incremental interval of 20%by weight.Permeability(k)for each mix was acquired at three relative densities(RD),i.e.,65%,75%and 85%,by using the constant head method.Then the results were further processed with machine learning(ML)models to validate the experimental data.The experimental study demonstrated that k would decrease with the increase in relative density and WFS content.A rise in RD from 65%to 85%resulted in a substantial reduction of up to 140%in the value of k.Moreover,the complete replacement of sand with WFS reduced the value of k by 36%,51%and 57%for RD of 65%,75%and 85%,respectively.The total dataset of 90 observations was divided at a ratio of 63/13/15 into training/validation/testing datasets for ML-AI modeling.Input variables include percentage of sand(BS),replacement with WFS,total head(H),time interval(t)and outflow(Q);and k is the output variable.The methods of artificial neural network(ANN),random forest(RF),decision tree(DT)and multi-linear regression(MLR)are used for k prediction.It is found that the random forest approach performed outstandingly in these methods,with an R_(2) value of 0.9955.The performance of all the proposed methods was compared and verified with Taylor’s diagram.Sensitivity analysis showed that Q and R_(D) were the most influential parameters for predicting k values.
文摘Objective:To evaluate the cardioprotective effects of sinomenine using the ischemia/reperfusion(I/R)rat model.Methods:Wistar rats were randomly divided into 6 groups:group Ⅰ with reperfusion,group Ⅱ perfused with sinomenine,group Ⅲ perfused with 5-hydroxydecanoate(5-HD),group Ⅳ perfused with 5-HD+sinomenine,group Ⅴ perfused with L-nitro arginine methyl ester(L-NAME),group Ⅵ perfused with L-NAME+sinomenine.Myocardial ischemia was induced by interrupting the aortic blood supply for 30 min,followed by reperfusion(55 min).Cardiac,hepatic,antioxidant,and inflammatory parameters were assessed.Additionally,endothelin,tissue factor,platelet-activating factor,plasminogen activator inhibitor,plasma fibrinogen,and thromboxane B2 were also analyzed.Results:Administration of 5-HD or L-NAME,used as the selective antagonist of mitoKATP and NO system,respectively,resulted in significantly increased levels of premature ventricular complexes,lactate dehydrogenase,ventricular fibrillation,ventricular tachycardia,and arrhythmia intensity(P<0.05).In contrast,sinomenine significantly reduced the level of troponin Ⅰ,lactate dehydrogenase,creatine kinase,and creatine kinase MB compared to the 5-HD group and the L-NAME group(P<0.05).Additionally,sinomenine significantly reduced malondialdehyde level and enhanced the levels of superoxide dismutase,glutathione peroxidase,catalase,and glutathione/glutathione disulfide ratio(P<0.05).It also significantly suppressed the levels of endothelin-1,platelet-activating factor,tissue factor,plasminogen activator inhibitor 1,thromboxane B2,and plasma fibrinogen(P<0.05).Conclusions:These results suggest that sinomenine exhibits significant cardioprotection effects against I/R-induced cardiac injury in rats.
文摘The correct diagnosis of heart disease can save lives,while the incorrect diagnosis can be lethal.The UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches,including deep learning.We used a dataset with 13 primary characteristics to carry out the research.Support vector machine and logistic regression algorithms are used to process the datasets,and the latter displays the highest accuracy in predicting coronary disease.Python programming is used to process the datasets.Multiple research initiatives have used machine learning to speed up the healthcare sector.We also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection risks.Using the accuracy and confusion matrix has resulted in some favorable outcomes.To get the best results,the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine(SVM)classification.
文摘In Computer-Aided Detection(CAD)brain disease classification is a vital issue.Alzheimer’s Disease(AD)and brain tumors are the primary reasons of death.The studies of these diseases are carried out by Magnetic Resonance Imaging(MRI),Positron Emission Tomography(PET),and Computed Tomography(CT)scans which require expertise to understand the modality.The disease is the most prevalent in the elderly and can be fatal in its later stages.The result can be determined by calculating the mini-mental state exam score,following which the MRI scan of the brain is successful.Apart from that,various classification algorithms,such as machine learning and deep learning,are useful for diagnosing MRI scans.However,they do have some limitations in terms of accuracy.This paper proposes some insightful pre-processing methods that significantly improve the classification performance of these MRI images.Additionally,it reduced the time it took to train the model of various pre-existing learning algorithms.A dataset was obtained from Alzheimer’s Disease Neurological Initiative(ADNI)and converted from a 4D format to a 2D format.Selective clipping,grayscale image conversion,and histogram equalization techniques were used to pre-process the images.After pre-processing,we proposed three learning algorithms for AD classification,that is random forest,XGBoost,and Convolution Neural Networks(CNN).Results are computed on dataset and show that it outperformed with exiting work in terms of accuracy is 97.57%and sensitivity is 97.60%.
文摘The objective of this paper is to provide a better rendition of Generation Z purchase intentions of retail products through Facebook.The study gyrated around the favorable attitude formation of Generation Z translating into intentions to purchase retail products through Facebook.The role of antecedents of attitude,namely enjoyment,credibility,and peer communication was also explored.The main purpose was to analyze the F-commerce pervasiveness(retail purchases through Facebook)among Generation Z in India and how could it be materialized effectively.A conceptual fac¸ade was proposed after trotting out germane and urbane literature.The study focused exclusively on Generation Z population.The data were statistically analyzed using partial least squares structural equation modelling.The study found the proposed conceptual model had a high prediction power of Generation Z intentions to purchase retail products through Facebook verifying the materialization of F-commerce.Enjoyment,credibility,and peer communication were proved to be good predictors of attitude(R^(2)=0.589)and furthermore attitude was found to be a stellar antecedent to purchase intentions(R^(2)=0.540).
文摘We live in an age where everything around us is being created.Data generation rates are so scary,creating pressure to implement costly and straightforward data storage and recovery processes.MapReduce model functionality is used for creating a cluster parallel,distributed algorithm,and large datasets.The MapReduce strategy from Hadoop helps develop a community of non-commercial use to offer a new algorithm for resolving such problems for commercial applications as expected from this working algorithm with insights as a result of disproportionate or discriminatory Hadoop cluster results.Expected results are obtained in the work and the exam conducted under this job;many of them are scheduled to set schedules,match matrices’data positions,clustering before determining to click,and accurate mapping and internal reliability to be closed together to avoid running and execution times.Mapper output and proponents have been implemented,and the map has been used to reduce the function.The execution input key/value pair and output key/value pair have been set.This paper focuses on evaluating this technique for the efficient retrieval of large volumes of data.The technique allows for capabilities to inform a massive database of information,from storage and indexing techniques to the distribution of queries,scalability,and performance in heterogeneous environments.The results show that the proposed work reduces the data processing time by 30%.
